1. Field of the Invention
This invention relates to processing of beamformed signals, and in particular to post-filtering of beamformed signals.
2. Related Art
Background noise is often a problem in audio communication between two or more parties, such as radio or cellular communication. Background noise in noisy environments directly affects the quality and intelligibility of voice conversations, and in the worst cases, the background noise may even lead to a complete breakdown of communication. With the use of hands-free voice communion devices in vehicles increasing, the quality and intelligibility of a voice communication signal is becoming more of an issue.
Hands-free telephones provide a comfortable and safe communication system of particular use in motor vehicles. The use of hands-free telephones in vehicles have also been promoted by laws enacted in many cities, such as Chicago, Ill., that requires the operator of a vehicle to use a hand-free device when making or receiving cellular telephones calls while operating the vehicle.
In addition to the quality of the voice communication signal between the parties on a telephone call, vehicles and communication devices are making use of voice commands. Voice commands often rely on voice recognition of words. If the voice command is issued in an environment with background noise, it may be misinterpreted or be unintelligible to the receiving device. Once again, the use of single channel noise reduction is desirable in such devices.
Approaches to single channel noise reduction methods employing spectral subtraction are known in the art. Such as, speech signals being divided into sub-bands by sub-band filtering where a noise reduction algorithm is applied to each of the sub-bands. These types of approaches, however, are limited to almost stationary noise perturbations and positive signal-to-noise distances. The processed speech signals are also distorted by these approaches, since the noise perturbations are not eliminated but rather spectral components that are affected by noise are damped. The intelligibility of speech signals is, thus, normally not improved sufficiently by these approaches.
Current multi-channel systems primarily make use of adaptive or non-adaptive beamformers, see, e.g., “Optimum Array Processing, Part IV of Detection, Estimation, and Modulation Theory” by H. L. van Trees, Wiley & Sons, New York 2002. The beamformer may combine multiple microphone input signals to one beamformed signal with an enhanced signal-to-noise ratio (SNR). Beamforming typically requires amplification of microphone signals corresponding to audio signals detected from a wanted signal direction by equal phase addition and attenuation of microphone signals corresponding to audio signals generated at positions in other direction.
The beamforming may be performed, in some approaches, by a fixed beamformer or an adaptive beamformer characterized by a permanent adaptation of processing parameters such as filter coefficients during operation (see e.g., “Adaptive beamforming for audio signal acquisition”, by Herbordt, W. and Kellermann, W., in “Adaptive signal processing: applications to real-world problems”, p. 155, Springer, Berlin 2003). By beamforming, the signal can be spatially filtered depending on the direction of the inclination of the sound detected by multiple microphones.
However, suppression of background noise in the context of beamforming is highly frequency-dependent and thus rather limited. Therefore, approaches that employ post-filters for processing the beamformed signals may be necessary in order to further reduce noise. But, such post-filters result in a time-dependent spectral weighting that is to be re-calculated in each signal frame. The determination of optimal weights, i.e., the filter characteristics, of the post-filters is still a major problem in the art. For instance, the weights are determined by means of coherence models or models based on the spatial energy. However, such relatively inflexible models do not allow for sufficiently suitable weights in the case of highly time-dependent strong noise perturbations.
Thus, there is a need for providing an approach for filtering background noise in the context of beamforming that overcomes the limitations of traditional post-filtering of the beamformed signal to reduce background noise.